1,973 件 ・ 関連度順
概要と使いどころ
Hostile self-review of recently written AI workflow code that then applies fixes (agents, tools, chat context, summarization, tokens, attachments). Use after generating AI systems code, or when asked to self-review and harden it. Not for PR peer review (code-review) or no-edit devil's-advocate attacks (adversarial-challenge). Prefer: adversarial-challenge → decide → this skill.
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Structured 3-phase peer review of a diff or PR — intent, issues by severity, then pointed merge-readiness questions and a verdict. Use for general code review. Not for AI workflow self-fix-and-edit (ai-systems-review) or no-edit devil's-advocate threat models (adversarial-challenge). Phase 3 asks questions only; it is not the adversarial-challenge skill.
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Transform transcripts, conversations, or raw notes into shareable content — either multiple social posts OR a single long-form article. Learns style from user's published content. Handles transcripts of any length (tested up to 5500+ lines / 90+ min). Use when user: (1) provides transcript/conversation/recording and wants social content, (2) asks to "turn recording/notes into posts", (3) mentions generating content from recordings/notes, (4) needs multiple publishable pieces from single source, (5) asks to write an article from a transcript — e.g. "write an article/long-form", "organize into an article", "write up this session", "turn this talk into a blog post", "deep dive". Social mode outputs 2-15 ready-to-publish pieces. Article mode outputs a structured long-form piece (3000-10000+ chars) written section-by-section.
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Aynı N ile iki farklı e kullanılarak şifrelenmiş mesaj, extended GCD ile çözülür
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RSA ve crypto için kafes (lattice) tabanlı saldırılar — Coppersmith, LLL/BKZ, Boneh-Durfee, Hastad broadcast, Franklin-Reiter, stereotyped message
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Searches the web for facts, news, and data.
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Check the latest Claude (Anthropic), OpenAI Codex, and Google Gemini model releases from official primary sources, update model selections in this dotfiles repo, and update the Claude Code, Codex, and Gemini CLIs through their configured managers. Every run checks both the model settings and CLI for the selected provider, then scans the invoking repository for hardcoded model IDs. Use when the user asks to "モデル更新", "モデルを最新に", "最新モデル確認", "Codex/Claude Code/Gemini CLI本体の更新", "model bump", "update models", or "update agent CLIs". Do NOT use for one-off model selection in a single conversation, general model questions, or unrelated package updates.
Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.
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Build an automated system to track adversary infrastructure using passive DNS, certificate transparency, WHOIS data, and IP enrichment to map and monitor threat actor command-and-control networks.
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Converts Claude Code skills to Gemini CLI extensions and vice versa. Use when the user wants to make a skill cross-platform compatible, port a skill between platforms, or create a universal extension that works on both Claude Code and Gemini CLI.
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Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".
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Orchestrates group discussions between installed BMAD agents, enabling natural multi-agent conversations where each agent is a real subagent with independent thinking. Use when user requests party mode, wants multiple agent perspectives, group discussion, roundtable, or multi-agent conversation about their project.
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Library of realistic adversarial attack vectors and anti-patterns to avoid. Contains examples of valid attacks and subtle gaming patterns to reject.
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Multi-Agent Adversarial Analysis System for code security
日本語の概要は準備中です。原文の説明を表示しています。
Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.
日本語の概要は準備中です。原文の説明を表示しています。
OpenAI API (developers.openai.com) の Realtime API と Audio エンドポイントのリファレンス。 WebRTC / WebSocket / SIP transport, sessions / conversations / events, client secrets (ephemeral keys), VAD (server_vad / semantic_vad), tools/MCP, speech-to-speech voice agents, speech-to-text (streaming / file transcription), text-to-speech, translation。
OpenAI API (developers.openai.com) の Responses API リファレンス。 text generation, embeddings, structured outputs, streaming, function calling, programmatic tool calling, conversation state, prompt caching, reasoning models, background mode, compaction, multi-agent。 webhooks, error codes, rate limits, Realtime API server controls。 公式 SDK (Python / JavaScript(JS) / .NET / Java / Go / Ruby), openai CLI。 Chat Completions / Assistants からの移行, deprecations。
MAGI System v2 — AoT 分解 + MELCHIOR/BALTHASAR/CASPAR 合議 + gabriel adversarial probe による 構造化意思決定フレームワーク。判断ポイント 2+ / 影響レイヤー 3+ / 選択肢 3+ で使用。 Use when facing complex decisions with multiple trade-offs or architectural choices.
MAGI System v2 — AoT 分解 + MELCHIOR/BALTHASAR/CASPAR 合議 + lam-harness:gabriel adversarial probe による 構造化意思決定フレームワーク。判断ポイント 2+ / 影響レイヤー 3+ / 選択肢 3+ で使用。 Use when facing complex decisions with multiple trade-offs or architectural choices.
Facilitate Kimball-style dimensional (data warehouse / BI) modeling sessions through conversation, producing DimML (Dimensional Modeling Language, YAML) as the primary artifact plus a bus-matrix + star-schema HTML. Use whenever the user wants to design fact tables, dimensions, a star schema, or a bus matrix — declaring grain, choosing SCD types, identifying conformed dimensions and measures (additivity) — by grilling one step at a time. Also invoke for refining an existing DimML, or when the user says "ディメンショナル・モデリング", "ディメンションモデリング", "スタースキーマ", "バスマトリクス", "ファクト表を設計したい", "グレインを決めたい", "DimML を育てたい", "Kimball で整理したい".
Facilitate DDD domain modeling sessions via EventStorming conversation and DML (Domain Modeling Language). Use whenever the user wants to model a business domain by discovering domain events, commands, aggregates, policies, read models, and bounded contexts through dialogue. Produces incrementally-built DML as the primary artifact, plus a Markdown session report. Also invoke for refining existing DML, mapping out a new feature's domain model, or when the user says "ドメインモデリングしたい", "イベントストーミング", "DDDで整理したい", "DMLを育てたい".
Infers person-to-person transmission from pathogen genomes using outbreaker2 (Campbell 2018), TransPhylo (Didelot 2017), phybreak (Klinkenberg 2017), BadTrIP (De Maio 2018), SCOTTI (De Maio 2016), BEASTLIER (Hall 2015), and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines outbreak clusters using pathogen-specific SNP thresholds (NOT a universal cutoff -- TB <=12 SNPs / Walker 2013; MRSA <=15 / Coll 2017; C. difficile <=2 / Eyre 2013; Klebsiella <=21 / Snitkin 2012), models within-host diversity and transmission bottlenecks (Worby-Lipsitch-Hanage 2014; McCrone 2018; Sobel Leonard 2017; Lythgoe 2021 SARS-CoV-2), integrates contact-tracing data, distinguishes generation interval from serial interval (Britton & Scalia Tomba 2019; Ali 2020), attributes source via Bayesian source attribution (Mather 2013 DT104; islandR), and reconciles transmission-network reconstruction with epi metadata. Use when investigating outbreaks for who-infected-whom, defining SNP-cluster outbreak definitions, accounting for unsampled intermediates, choosing between outbreaker2 (rich epi data) and TransPhylo (genomic-only after dated phylogeny), running source attribution between host populations, calling HIV-TRACE thresholds appropriate to the local subtype, or distinguishing recent transmission from reactivation in TB / chronic HIV.
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Generate React Spectrum UI components for Adobe Experience Cloud Shell SPAs and AEM UI Extensions. Provides patterns for pages, forms, data tables, dialogs, and navigation using @adobe/react-spectrum. Guides ExC Shell integration with @adobe/exc-app including runtime.done(), IMS token passthrough, and shell theming. Guides AEM UI Extension development with @adobe/uix-guest for Content Fragment Console, CF Editor, Universal Editor, and Assets View surfaces. Trigger on: building App Builder UI, React Spectrum components, ExC Shell pages, forms, data tables, dialogs, modals, navigation, theming, web-src, Spectrum design system, @adobe/exc-app, AEM extension, AEM UI extension, Content Fragment Console, Universal Editor extension, uix-guest, @adobe/uix-guest, extension points for AEM, customizing AEM surfaces.
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Security checks for repository ingestion — scans repos for poisoned examples, dependency and supply-chain attacks, file/path traversal, format-based attacks, and enforces quarantine-before-commit. Load as part of the secure-* skill sequence whenever an agent reads, ingests, or learns from a GitHub repository. Also load when the user asks to check a repo for poisoned code, scan dependencies, verify supply chain safety, check for path traversal, scan repo files for attacks, or audit a repo before ingestion. Covers Issues 3, 4, 7, 8 from the agent security threat model: poisoned training data, dependency attacks, file/path attacks, and format-based attacks.
日本語の概要は準備中です。原文の説明を表示しています。